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首页> 外文期刊>Powder Technology: An International Journal on the Science and Technology of Wet and Dry Particulate Systems >Random particle packing with large particle size variations using reduced-dimension algorithms
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Random particle packing with large particle size variations using reduced-dimension algorithms

机译:使用降维算法,具有大粒径变化的随机粒子堆积

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We present a reduced-dimension, ballistic deposition, Monte Carlo particle packing algorithm and discuss its application to the analysis of the microstructure of hard-sphere systems with broad particle size distributions. We extend our earlier approach (the "central string" algorithm) to a reduced-dimension, quasi-3D approach. Our results for monomodal hard-sphere packs exhibit a calculated packing fraction that is slightly less than the generally accepted value for a maximally random jammed state. The pair distribution functions obtained from simulations of composite structures with large particle size differences demonstrate that the algorithm provides information heretofore not attainable with existing simulation methods, and yields detailed understanding of the microstructure of these composite systems. (c) 2006 Elsevier B.V. All rights reserved.
机译:我们提出了一种降维,弹道沉积,蒙特卡洛粒子堆积算法,并讨论了其在分析具有宽粒径分布的硬球系统微观结构中的应用。我们将先前的方法(“中心字符串”算法)扩展为降维的准3D方法。我们对单峰硬球包装的结果显示,计算出的包装分数略小于最大随机阻塞状态的公认值。从具有大粒径差异的复合结构的模拟中获得的对分布函数表明,该算法提供了迄今为止用现有的模拟方法无法获得的信息,并对这些复合系统的微观结构有了详细的了解。 (c)2006 Elsevier B.V.保留所有权利。

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